Reliable peak detection remains a bottleneck in size-exclusion chromatography (SEC) as overlapping signals, drifting baselines, and analyst variability limit reproducibility. As SEC is a routine release and comparability assay and its interpretation depends on peak morphology and context, machine learning methods are well-suited to improve reproducibility at scale. We present the Peak Feature Extractor 1 (PFE-1), a one-dimensional encoder-only transformer trained on millions of synthetic chromatograms generated by a simulator statistically calibrated to routine SEC data from antibodies and related large-molecule species. PFE-1 outputs probabilistic region and event predictions that are aggregated through a transparent rule-based procedure into interpretable peak boxes. We evaluate PFE-1 on synthetic benchmarks and on a curated real SEC benchmark, reporting window-level precision/recall/F1 and box-level agreement via an intensity-weighted box loss aligned with routine process annotations. Across these evaluations, PFE-1 outperforms convolutional and derivative-based baselines, with the largest gains observed under more challenging overlap and morphology conditions. On synthetic data, PFE-1 achieves substantially higher box-level agreement than both baselines; on the curated real SEC benchmark, it likewise achieves the strongest box-level agreement while requiring no sample-specific inputs (e.g., expected peak windows). We provide a reproducible and extensible SEC-specific framework for chromatographic peak detection that supports a more consistent peak interpretation in routine analytical workflows.
Walter et al. (Tue,) studied this question.